
# Importing the necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import scipy.stats as stats
from scipy.stats import ttest_ind
from statsmodels.stats.proportion import proportions_ztest
from scipy.stats import chi2_contingency
from scipy.stats import shapiro
from statsmodels.stats.weightstats import ztest as ztest
%matplotlib inline
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, roc_auc_score, roc_curve, auc
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.naive_bayes import GaussianNB


# mounting to drive
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive


# reading the datafile
data = pd.read_csv('/content/drive/MyDrive/INNHotelsGroup.csv')


# making a copy of the dataset
df = data.copy()


# check first five rows
df.head()


# check last five rows of the dataset
df.tail()


# check no. of rows and columns
df.shape

(36275, 19)


# Analyzing the data types present in dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB


# checking if there are duplicate entries in the dataset
df.duplicated().sum()

0


# checking if there are missing values
df.isnull().sum()


#  shape of the data set
df.shape

(36275, 19)


# statistical summary
df.describe().T # Transpose for readability


# checking unique values for each column
for column in df.columns:
  print("{} : {}".format(column,df[column].unique()))
  print("-"*90)

Booking_ID : ['INN00001' 'INN00002' 'INN00003' ... 'INN36273' 'INN36274' 'INN36275']
------------------------------------------------------------------------------------------
no_of_adults : [2 1 3 0 4]
------------------------------------------------------------------------------------------
no_of_children : [ 0  2  1  3 10  9]
------------------------------------------------------------------------------------------
no_of_weekend_nights : [1 2 0 4 3 6 5 7]
------------------------------------------------------------------------------------------
no_of_week_nights : [ 2  3  1  4  5  0 10  6 11  7 15  9 13  8 14 12 17 16]
------------------------------------------------------------------------------------------
type_of_meal_plan : ['Meal Plan 1' 'Not Selected' 'Meal Plan 2' 'Meal Plan 3']
------------------------------------------------------------------------------------------
required_car_parking_space : [0 1]
------------------------------------------------------------------------------------------
room_type_reserved : ['Room_Type 1' 'Room_Type 4' 'Room_Type 2' 'Room_Type 6' 'Room_Type 5'
 'Room_Type 7' 'Room_Type 3']
------------------------------------------------------------------------------------------
lead_time : [224   5   1 211  48 346  34  83 121  44   0  35  30  95  47 256  99  12
 122   2  37 130  60  56   3 107  72  23 289 247 186  64  96  41  55 146
  32  57   7 124 169   6  51  13 100 139 117  39  86  19 192 179  26  74
 143 177  18 267 155  46 128  20  40 196 188  17 110  68  73  92 171 134
 320 118 189  16  24   8  10 182 116 123 105 443 317 286 148  14  85  25
  28  80  11 162  82  27 245 266 112  88  69 273   4  97  31  62 197 280
 185 160 104  22 292 109 126 303  81  54  15 161 147  87 127 418 156  58
 433 111 195 119  59  78 335 103  70  76 144  49  77  36  79  21  33 164
 152  43 102  71 209  93  53 302 239  45 167 113  84   9 166 174  61 151
  52  67 282  38 175  89 133  65  66  50 159 386 115 237 125  91  29 221
 213 198  75 180 236 120 230  63 136 309 157 268 217  94 305  98  42 154
 330 137 184 232 304 114 257 265 191 101 259 149 170 271 207 108 210 222
 296 194 145 153 275 158 301 349 200 315 181 263 176 141 270 150 359 244
 219 142 138 276 178 163 377 290 216 226 258 254 193 131 208 215 190 381
 231 248 106 308 140 173 168 172  90 249 205 129 212 135 220 277 253 132
 183 255 223 336 288 229 319 199 203 228 246 235 294 281 202 361 287 291
 313 206 269 279 261 214 274 250 187 240 241 323 322 227 225 233 338 283
 327 204 352 165 251 299 314 285 238 328 278 332 243 201 307 272 252 242
 284 297 324 260 262 326 295 218 234 353 300 355 306 298 331 341 318 333
 372 311 310 345 264 325 293 348 350 351]
------------------------------------------------------------------------------------------
arrival_year : [2017 2018]
------------------------------------------------------------------------------------------
arrival_month : [10 11  2  5  4  9 12  7  6  8  3  1]
------------------------------------------------------------------------------------------
arrival_date : [ 2  6 28 20 11 13 15 26 18 30  5 10  4 25 22 21 19 17  7  9 27  1 29 16
  3 24 14 31 23  8 12]
------------------------------------------------------------------------------------------
market_segment_type : ['Offline' 'Online' 'Corporate' 'Aviation' 'Complementary']
------------------------------------------------------------------------------------------
repeated_guest : [0 1]
------------------------------------------------------------------------------------------
no_of_previous_cancellations : [ 0  3  1  2 11  4  5 13  6]
------------------------------------------------------------------------------------------
no_of_previous_bookings_not_canceled : [ 0  5  1  3  4 12 19  2 15 17  7 20 16 50 13  6 14 34 18  8 10 23 11 49
 47 53  9 33 22 24 52 21 48 28 39 25 31 38 26 51 42 37 35 56 44 27 32 55
 45 30 57 46 54 43 58 41 29 40 36]
------------------------------------------------------------------------------------------
avg_price_per_room : [ 65.   106.68  60.   ... 118.43 137.25 167.8 ]
------------------------------------------------------------------------------------------
no_of_special_requests : [0 1 3 2 4 5]
------------------------------------------------------------------------------------------
booking_status : ['Not_Canceled' 'Canceled']
------------------------------------------------------------------------------------------


# visualizing the number of adults using bar plot
plt.figure(figsize=(7,4)) # size of the figure
sns.countplot(x='no_of_adults', data=df, palette="pastel")
plt.title('Number of adults') # title of the plot
plt.xlabel('Number of adults') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# visualizing no of children using a bar plot
plt.figure(figsize=(7,5)) # size of the figure
sns.countplot(x='no_of_children', data=df, palette="pastel")
plt.title('Number of children') # title of the plot
plt.xlabel('Number of children') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# visualizing using a histogram for no of weekend nights
plt.figure(figsize=(7,4))
sns.histplot(df['no_of_weekend_nights'],kde=True , bins = 10, color="#adf7b6")
plt.title('Distribution of weekend nights (kernal density estimate)') # title of the plot
plt.xlabel('No of weekend nights [Saturday or Sunday]') # label of x-axis
plt.ylabel('count') # label of y-axis
plt.show() # show the plot

plt.figure(figsize=(7,4))
sns.histplot(df['no_of_weekend_nights'], bins = 10, color="#a0ced9")
plt.title('Distribution of weekend nights') # title of the plot
plt.xlabel('No of weekend nights [Saturday or Sunday]') # label of x-axis
plt.ylabel('count') # label of y-axis
plt.show() # show the plot


#visualizing no of week nights stay by customer using histogram
plt.figure(figsize=(7,4)) # size of the figure
sns.histplot(df['no_of_week_nights'], bins=10,kde=True, color="#adf7b6")
plt.title('Distribution of week nights (kernal density estimate)') # title of the plot
plt.xlabel('No of week nights [Monday to Friday]') # label of x-axis
plt.ylabel('count') # label of y-axis
plt.show() # show the plot


plt.figure(figsize=(7,4)) # size of the figure
sns.histplot(df['no_of_week_nights'], bins=10, color="#a0ced9")
plt.title('Distribution of week nights') # title of the plot
plt.xlabel('No of week nights [Monday to Friday]') # label of x-axis
plt.ylabel('count') # label of y-axis
plt.show() # show the plot


# visualizing the type of meal plan selected by the customers using countplot
plt.figure(figsize=(7,4)) # size of the figure
sns.countplot(x='type_of_meal_plan', data=df, palette="pastel")
plt.title('Type of meal plan') # title of the plot
plt.xlabel('Type of meal plan')  # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# visualizing the required car parking space using a pie chart
plt.figure(figsize=(6,6))
df.required_car_parking_space.value_counts().plot(kind='pie', autopct='%1.0f%%', colors = ['#a0ced9', '#adf7b6'])
plt.title('Required car parking space') # title of the plot
plt.show()


# visualizing the room type reserved by the customers using countplot
plt.figure(figsize=(8,5)) # size of the figure
sns.countplot(x='room_type_reserved', data=df, palette="pastel")
plt.title('Room type reserved') # title of the plot
plt.xticks(rotation=90) # rotate the x-axis labels
plt.xlabel('Room type reserved') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# visualizing the lead time using a box plot
plt.figure(figsize=(7,4))
sns.boxplot(y='lead_time', data=df, color = '#a0ced9')
plt.title('Lead time') # title of the plot
plt.xlabel('Lead time') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show()


# visualization of arrival year using bar plot
plt.figure(figsize=(7,4)) # size of the figure
sns.countplot(x='arrival_year', data=df, palette="pastel")
plt.title('Arrival year') # title of the plot
plt.xlabel('Year') # label of x-axis
plt.ylabel('Count')  # label of y-axis
plt.show() # show the plot


# visualizing the arrival month using a line plot
plt.figure(figsize=(7,5))
sns.lineplot(x='arrival_month', y=df.arrival_month.value_counts(), data=df, color="#ff7477")
plt.title('Arrival month')
plt.xlabel('Month')
plt.ylabel('Count')
plt.show()


# visualizing the date in which customers arrive using line pot
plt.figure(figsize=(15,4)) # size of the figure
sns.lineplot(x='arrival_date', y=df.arrival_date.value_counts(), data=df, color="#ff7477")
plt.title('Arrival date') # title of the plot
plt.xlabel('Date') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# visualizating market segment type using barplot

plt.figure(figsize=(7,4))
sns.countplot(x='market_segment_type', data=df, palette="pastel")
plt.title('Market segment type')
plt.xlabel('Market segment type')
plt.ylabel('Count')
plt.show()


# visualizing repeated guests using pie chrt

plt.figure(figsize=(6,6)) # size of the figure
df.repeated_guest.value_counts().plot(kind='pie', colors = ['#a0ced9', '#adf7b6'])
plt.title('Repeated guest') # title of the plot
plt.show()


# Visualizing the cancellations made previously using a histogram
plt.figure(figsize=(7,4)) # size of the figure
sns.histplot(df['no_of_previous_cancellations'], bins=10 , color="#a0ced9")
plt.title('No of previous cancellations') # title of the plot
plt.xlabel('No of previous cancellations')  # label of x-axis
plt.ylabel('count')  # label of y-axis
plt.show()


# visualization of previous bookings not cancelled
plt.figure(figsize=(7,4)) # size of the figure
sns.histplot(df['no_of_previous_bookings_not_canceled'], bins=10 , color="#a0ced9")
plt.title('No of previous bookings not cancellled') # title of the plot
plt.xlabel('No of previous bookings not cancelled') # label of x-axis
plt.ylabel('count') # label of y-axis
plt.show()


# visualizing the average price per room using a histogram
plt.figure(figsize=(7,4)) # size of the figure
sns.histplot(df['avg_price_per_room'], bins=10 , color="#a0ced9")
plt.title('Average price per room') # title of the plot
plt.xlabel('Average price per room') # label of x-axis
plt.ylabel('count') # label of y-axis
plt.show()


# visualizing avg price using boxplot
plt.figure(figsize=(7,4)) # size of the figure
sns.boxplot(y='avg_price_per_room', data=df, color = '#a0ced9')
plt.title('Average price per room') # title of the plot
plt.xlabel('Average price per room') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show()


# visualizing the no of special requests made by customers using bar plot
plt.figure(figsize=(7,4)) # size of the figure
sns.countplot(x='no_of_special_requests', data=df, palette="pastel")
plt.title('No of special requests') # title of the plot
plt.xlabel('No of special requests') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show()


# visualizing the booking status using a countplot
plt.figure(figsize=(7,4)) # size of the figure
sns.countplot(x='booking_status', data=df, palette="pastel")
plt.title('Booking status') # title of the plot
plt.xlabel('Booking status') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# boxplot to visualize the distribution of average room prices for different numbers of adults.
plt.figure(figsize=(8, 6)) # size of the figure
sns.boxplot(x='no_of_adults', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Adults') # title of the plot
plt.xlabel('Number of Adults') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


#boxplot to visualize the distribution of average room prices for different numbers of children.
plt.figure(figsize=(8, 6)) # size of the figure
sns.boxplot(x='no_of_children', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Children') # title of the plot
plt.xlabel('Number of Children') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show()


# Analyzing the relation between stay on weekend night and room price with boxplot
plt.figure(figsize=(10, 6)) # size of the figure
sns.boxplot(x='no_of_weekend_nights', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Weekend Nights') # title of the plot
plt.xlabel('Number of Weekend Nights') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show()


# Analyzing the effect of lead time over the average price per room

plt.figure(figsize=(8, 6)) # size of the figure
sns.scatterplot(x='lead_time', y='avg_price_per_room', data=df, color = '#a0ced9')
plt.title('Lead Time vs Average Room Price') # title of the plot
plt.xlabel('Lead Time (Days)') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.grid(True) # show the grid
plt.show()


# Analyzing the relation between stay on week night and room price with boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(x='no_of_week_nights', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Week Nights')
plt.xlabel('Number of Week Nights')
plt.ylabel('Average Room Price')
plt.show()


# Analyze if bookings made in advance tend to have more special requests
plt.figure(figsize=(8, 6)) # size of the figure
sns.boxplot(x='no_of_special_requests', y='lead_time', data=df, palette='pastel')
plt.title('Lead Time vs Number of Special Requests') # title of the plot
plt.xlabel('Number of Special Requests') # label of x-axis
plt.ylabel('Lead Time (Days)') # label of y-axis
plt.show() # show the plot


# box plot analysis between the number of special requests and room prices
plt.figure(figsize=(8, 6)) # size of the figure
sns.boxplot(x='no_of_special_requests', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Special Requests') # title of the plot
plt.xlabel('Number of Special Requests') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show()


# Analyzing if there is any sesonality
plt.figure(figsize=(12, 6)) # size of the figure
sns.boxplot(x='arrival_month', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Arrival Month') # title of the plot
plt.xlabel('Arrival Month') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


# scattrplot Room price and arrival date

plt.figure(figsize=(15, 6)) # size of the figure
sns.scatterplot(x='arrival_date', y='avg_price_per_room', data=df, color='#a0ced9')
plt.title('Average Room Price vs Arrival Date') # title of the plot
plt.xlabel('Arrival Date') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.grid(True) # show the grid
plt.show()


# Analyzing if repeated guests tend to pay more or less per room
plt.figure(figsize=(8, 5)) # size of the figure
sns.boxplot(x='repeated_guest', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Repeated Guest') # title of the plot
plt.xlabel('Repeated Guest (0 = No, 1 = Yes)') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show()


# Analyze the relation betwen lead time and previous cancellations
plt.figure(figsize=(8, 6)) # size of the figure
sns.scatterplot(x='no_of_previous_cancellations', y='lead_time', data=df, color='#a0ced9')
plt.title('Number of Previous Cancellations vs Lead Time') # title of the plot
plt.xlabel('Number of Previous Cancellations')  # label of x-axis
plt.ylabel('Lead Time (Days)') # label of y-axis
plt.grid(True) # show the grid
plt.show()


# Analyzing the relation between number of previous bookings not cancelled and room price with boxplot
plt.figure(figsize=(20,9)) # size of the figure
sns.boxplot(x='no_of_previous_bookings_not_canceled', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Previous Bookings Not Cancelled') # title of the plot
plt.xlabel('Number of Previous Bookings Not Cancelled') # label of x-axis
plt.xticks(rotation=90) # rotate the x-axis labels for better visibility
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


# Analyzing the relation between market segmentation and price of the room
plt.figure(figsize=(10, 6)) # size of the figure
sns.boxplot(x='market_segment_type', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Market Segment Type') # title of the plot
plt.xlabel('Market Segment Type') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


# Analyzing the frequency of booking with market segment
plt.figure(figsize=(10,5 )) # size of the figure
sns.countplot(x='market_segment_type', hue='booking_status', data=df, palette='pastel')
plt.title('Booking Status by Market Segment') # title of the plot
plt.xlabel('Market Segment Type') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# Analyzing the booking status across no. of special requests made.
plt.figure(figsize=(8, 5)) # size of the figure
sns.boxplot(x='booking_status', y='no_of_special_requests', data=df, palette='pastel')
plt.title('Number of Special Requests vs Booking Status') # title of the plot
plt.xlabel('Booking Status')  # label of x-axis
plt.ylabel('Number of Special Requests') # label of y-axis
plt.show() # show the plot


# Analyzing how special requests influence the pric charged per room
plt.figure(figsize=(8, 6)) # size of the figure
sns.boxplot(x='no_of_special_requests', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Special Requests') # title of the plot
plt.xlabel('Number of Special Requests') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


# Analyzing the effect of booking status on price charged
plt.figure(figsize=(8, 5)) # size of the figure
sns.boxplot(x='booking_status', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Booking Status') # title of the plot
plt.xlabel('Booking Status')  # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


# Analying the booking staus and lead time using boxplot
plt.figure(figsize=(8,4 )) # size of the figure
sns.boxplot(x='booking_status', y='lead_time', data=df, palette='pastel')
plt.title('Lead Time vs Booking Status') # title of the plot
plt.xlabel('Booking Status') # label of x-axis
plt.ylabel('Lead Time (Days)') # label of y-axis
plt.show() # show the plot


# Analyzing using  countplot for Arrival month and Booking status
plt.figure(figsize=(12, 6)) # size of the figure
sns.countplot(x='arrival_month', hue='booking_status', data=df, palette='pastel')
plt.title('Booking Status by Arrival Month') # title of the plot
plt.xlabel('Arrival Month') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# Analyzing booking status and repeated guest
plt.figure(figsize=(8, 5)) # size of the figure
sns.countplot(x='repeated_guest', hue='booking_status', data=df, palette='pastel')
plt.title('Booking Status by Repeated Guest') # title of the plot
plt.xlabel('Repeated Guest (0 = No, 1 = Yes)') # label of x-axis
plt.ylabel('Count') # label of y-axis
plt.show() # show the plot


# Analyzing the the affect of month on the price charged
plt.figure(figsize=(12, 6)) # size of the figure
sns.boxplot(x='arrival_month', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Arrival Month') # title of the plot
plt.xlabel('Arrival Month') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show()


# Analyzing the the affect of month on the price charged
plt.figure(figsize=(12, 6)) # size of the figure
sns.barplot(x='arrival_month', y='avg_price_per_room', data=df, palette='pastel')
plt.title('Average Room Price vs Arrival Month') # title of the plot
plt.xlabel('Arrival Month') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


# clustered bar plot of Avg Price Per Room vs Market Segment Type vs Booking Status
plt.figure(figsize=(12, 6)) # size of the figure
sns.barplot(x='market_segment_type', y='avg_price_per_room', hue='booking_status', data=df, palette='pastel')
plt.title('Average Room Price vs Market Segment Type vs Booking Status') # title of the plot
plt.xlabel('Market Segment Type') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.xticks(rotation=90) # rotate the x-axis labels for better visibility
plt.show() # show the plot


# Analyzing using line plot for Average Price Per Room vs Arrival Month vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.lineplot(x='arrival_month', y='avg_price_per_room', hue='booking_status', data=df, marker='o')
plt.title('Average Room Price vs Arrival Month vs Booking Status') # title of the plot
plt.xlabel('Arrival Month') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.xticks(rotation=90) # rotate the x-axis labels for better visibility
plt.show() # show the plot


# visualizing the relation with  scatterplot of  Average Price Per Room vs Lead Time vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.scatterplot(x='lead_time', y='avg_price_per_room', hue='booking_status', data=df)
plt.title('Average Room Price vs Lead Time vs Booking Status')  # title of the plot
plt.xlabel('Lead Time')  # label of x-axis
plt.ylabel('Average Room Price')  # label of y-axis
plt.show()  # show the plot


# visualizing with clustered bar plot of No. of Adults vs Avg Price Per Room vs Booking Status
plt.figure(figsize=(12, 6)) # size of the figure
sns.barplot(x='no_of_adults', y='avg_price_per_room', hue='booking_status', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Adults vs Booking Status') # title of the plot
plt.xlabel('Number of Adults') # label of x-axis
plt.ylabel('Average Room Price') # label of y-axis
plt.show() # show the plot


#  boxplot of Avg Price Per Room vs Room Type Reserved vs Booking Status
plt.figure(figsize=(12,7 ))  # size of the figure
sns.boxplot(y='room_type_reserved', x='avg_price_per_room', hue='booking_status', data=df, palette='pastel')
plt.title('Average Room Price vs Room Type Reserved vs Booking Status')  # title of the plot
plt.xlabel('Room Type Reserved')  # label of x-axis
plt.ylabel('Average Room Price')  # label of y-axis
plt.xticks(rotation=90)  # rotate the x-axis labels for better visibility
plt.show()  # show the plot


# visualizing with grouped bar plot of No. of Special Requests vs Avg Price Per Room vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.barplot(x='no_of_special_requests', y='avg_price_per_room', hue='booking_status', data=df, palette='pastel')
plt.title('Average Room Price vs Number of Special Requests vs Booking Status')  # title of the plot
plt.xlabel('Number of Special Requests')  # label of x-axis
plt.ylabel('Average Room Price')  # label of y-axis
plt.show()  # show the plot


# Analyzing relationship with heatmap of Arrival Year vs Arrival Month vs Avg Price Per Room
heatmap_data = df.pivot_table(values='avg_price_per_room', index='arrival_year', columns='arrival_month', aggfunc='mean')
plt.figure(figsize=(12, 8))  # size of the figure
sns.heatmap(heatmap_data, annot=True, fmt=".2f", cmap="viridis")
plt.title('Average Room Price by Arrival Year and Month')  # title of the plot
plt.xlabel('Arrival Month')  # label of x-axis
plt.ylabel('Arrival Year')  # label of y-axis
plt.show()  # show the plot


# prompt:  visualizing with scatterplot for Lead Time vs No. of Weekend Nights vs Avg Price Per Room

plt.figure(figsize=(12,6))  # size of the figure
sns.scatterplot(x='lead_time', y='no_of_weekend_nights', hue='avg_price_per_room', data=df)
plt.title('Lead Time vs No. of Weekend Nights vs Avg Price Per Room')  # title of the plot
plt.xlabel('Lead Time')  # label of x-axis
plt.ylabel('No. of Weekend Nights')  # label of y-axis
plt.show()  # show the plot


# Visualizing with box plot of Lead Time vs Market Segment Type vs Booking Status
plt.figure(figsize=(12, 8))  # size of the figure
sns.boxplot(x='market_segment_type', y='lead_time', hue='booking_status', data=df, palette='pastel')
plt.title('Lead Time vs Market Segment Type vs Booking Status')  # title of the plot
plt.xlabel('Market Segment Type')  # label of x-axis
plt.ylabel('Lead Time')  # label of y-axis
plt.xticks(rotation=90)  # rotate the x-axis labels for better visibility
plt.show()  # show the plot


# Visualizing with  bar plot of No. of Children vs No. of Special Requests vs Booking Status
plt.figure(figsize=(10, 6))  # size of the figure
sns.countplot(x='no_of_children', hue='booking_status', data=df, palette='pastel')
plt.title('No. of Children vs No. of Special Requests vs Booking Status')  # title of the plot
plt.xlabel('No. of Children')  # label of x-axis
plt.ylabel('count of customers with special requests')  # label of y-axis
plt.show()  # show the plot


# Analyzing with scatterplot of No. of Weekend Nights vs No. of Week Nights vs Avg Price Per Room
plt.figure(figsize=(12, 6))  # size of the figure
sns.scatterplot(x='no_of_weekend_nights', y='no_of_week_nights', hue='avg_price_per_room', data=df)
plt.title('No. of Weekend Nights vs No. of Week Nights vs Avg Price Per Room')  # title of the plot
plt.xlabel('No. of Weekend Nights')  # label of x-axis
plt.ylabel('No. of Week Nights')  # label of y-axis
plt.show()  # show the plot


# Analyzing relation with heatmap of No. of Weekend Nights vs No. of Week Nights vs Avg Price Per Room
heatmap_data = df.pivot_table(values='avg_price_per_room', index='no_of_weekend_nights', columns='no_of_week_nights', aggfunc='mean')
plt.figure(figsize=(12, 8))  # size of the figure
sns.heatmap(heatmap_data, annot=True, fmt=".2f", cmap="viridis")
plt.title('Average Room Price by Weekend Nights and Week Nights')  # title of the plot
plt.xlabel('Number of Week Nights')  # label of x-axis
plt.ylabel('Number of Weekend Nights')  # label of y-axis
plt.show()  # show the plot


# Visualizing with scatterplot of No. of Previous Cancellations vs Lead Time vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.scatterplot(x='no_of_previous_cancellations', y='lead_time', hue='booking_status', data=df)
plt.title('No. of Previous Cancellations vs Lead Time vs Booking Status')  # title of the plot
plt.xlabel('No. of Previous Cancellations')  # label of x-axis
plt.ylabel('Lead Time')  # label of y-axis
plt.show()  # show the plot


# Visualizing with barplot of No. of Previous Cancellations vs Lead Time vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.barplot(x='no_of_previous_cancellations', y='lead_time', hue='booking_status', data=df, palette='pastel')
plt.title('No. of Previous Cancellations vs Lead Time vs Booking Status')  # title of the plot
plt.xlabel('No. of Previous Cancellations')  # label of x-axis
plt.ylabel('Lead Time')  # label of y-axis
plt.show()  # show the plot


# Analyzing relationship with heatmap of Market Segment Type vs Room Type Reserved vs Avg Price Per Room
heatmap_data = df.pivot_table(values='avg_price_per_room', index='market_segment_type', columns='room_type_reserved', aggfunc='mean')
plt.figure(figsize=(12, 8))  # size of the figure
sns.heatmap(heatmap_data, annot=True, fmt=".2f", cmap="viridis")
plt.title('Average Room Price by Market Segment Type and Room Type Reserved')  # title of the plot
plt.xlabel('Room Type Reserved')  # label of x-axis
plt.xticks(rotation=90)  # rotate the x-axis labels for better visibility
plt.ylabel('Market Segment Type')  # label of y-axis
plt.show()  # show the plot


# Visualize using a barplot of Arrival Month vs No. of Special Requests vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.barplot(x='arrival_month', y='no_of_special_requests', hue='booking_status', data=df, palette='pastel')
plt.title('Arrival Month vs No. of Special Requests vs Booking Status')  # title of the plot
plt.xlabel('Arrival Month')  # label of x-axis
plt.ylabel('No. of Special Requests')  # label of y-axis
plt.xticks(rotation=90)  # rotate the x-axis labels for better visibility
plt.show()  # show the plot


# Analyzing relationship with barplot of Arrival Month vs Lead Time vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.barplot(x='arrival_month', y='lead_time', hue='booking_status', data=df, palette='pastel')
plt.title('Arrival Month vs Lead Time vs Booking Status')  # title of the plot
plt.xlabel('Arrival Month')  # label of x-axis
plt.ylabel('Lead Time')  # label of y-axis
plt.xticks(rotation=90)  # rotate the x-axis labels for better visibility
plt.show()  # show the plot


# analyzing with barplot of Market Segment Type vs Arrival Month vs Booking Status
plt.figure(figsize=(12, 6))  # size of the figure
sns.countplot(x='arrival_month', hue='market_segment_type', data=df, palette='pastel')
plt.title('Market Segment Type vs Arrival Month vs Booking Status')  # title of the plot
plt.xlabel('Arrival Month')  # label of x-axis
plt.ylabel('Count')  # label of y-axis
plt.xticks(rotation=90)  # rotate the x-axis labels for better visibility
plt.show()  # show the plot


# analyzing with scatterplot of No. of Adults vs Lead Time vs Avg Price Per Room
plt.figure(figsize=(12, 6))  # size of the figure
sns.scatterplot(x='no_of_adults', y='lead_time', hue='avg_price_per_room', data=df)
plt.title('No. of Adults vs Lead Time vs Avg Price Per Room')  # title of the plot
plt.xlabel('No. of Adults')  # label of x-axis
plt.ylabel('Lead Time')  # label of y-axis
plt.show()  # show the plot


# To find the busiest months, let's calculate the number of bookings per month.
busiest_months = df.groupby('arrival_month')['Booking_ID'].count().reset_index()
busiest_months.columns = ['Month', 'No_of_Bookings']
# Sort by number of bookings in descending order to identify the busiest months
busiest_months = busiest_months.sort_values(by='No_of_Bookings', ascending=False)

busiest_months


# To determine which market segment most guests come from, let's calculate the count of bookings per market segment.
market_segment_counts = data['market_segment_type'].value_counts()
market_segment_counts


# Group by market segment and calculate the mean room price
room_price_by_segment = data.groupby('market_segment_type')['avg_price_per_room'].mean().reset_index()
print(room_price_by_segment)

  market_segment_type  avg_price_per_room
0            Aviation          100.704000
1       Complementary            3.141765
2           Corporate           82.911740
3             Offline           91.632679
4              Online          112.256855


# Total number of bookings
total_bookings = len(df)

# Number of canceled bookings
canceled_bookings = len(df[df['booking_status'] == 'Canceled'])

# Calculate the percentage of canceled bookings
percentage_canceled = (canceled_bookings / total_bookings) * 100
print("Percentage of canceled bookings:", percentage_canceled)

Percentage of canceled bookings: 32.76361130254997


# Filter for repeating guests
repeating_guests = df[df['repeated_guest'] == 1]

# Total number of repeating guests
total_repeating_guests = len(repeating_guests)

# Number of repeating guests who canceled
repeating_guests_canceled = len(repeating_guests[repeating_guests['booking_status'] == 'Canceled'])

# Calculate the percentage of repeating guests who canceled
percentage_repeating_guests_canceled = (repeating_guests_canceled / total_repeating_guests) * 100
print(f"Percentage of repeating guests who canceled: {percentage_repeating_guests_canceled:.2f}%")

Percentage of repeating guests who canceled: 1.72%


# Guests with special requests
guests_with_requests = df[df['no_of_special_requests'] > 0]

# Guests without special requests
guests_without_requests = df[df['no_of_special_requests'] == 0]

# Calculate the cancellation rate for both groups
cancellation_rate_with_requests = (len(guests_with_requests[guests_with_requests['booking_status'] == 'Canceled'])
                                   / len(guests_with_requests)) * 100

cancellation_rate_without_requests = (len(guests_without_requests[guests_without_requests['booking_status'] == 'Canceled'])
                                      / len(guests_without_requests)) * 100
print(f"Cancellation rate for guests with special requests: {cancellation_rate_with_requests:.2f}%")
print(f"Cancellation rate for guests without special requests: {cancellation_rate_without_requests:.2f}%")

Cancellation rate for guests with special requests: 20.24%
Cancellation rate for guests without special requests: 43.21%


data= df.copy()


data.loc[data['booking_status']=='Not_Canceled','booking_status'] = False
data.loc[data['booking_status']=='Canceled','booking_status'] = True


numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
# drop column because they were either time, or not helpful
numeric_columns.remove("arrival_year")


plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    data.boxplot()

plt.xticks(rotation=45)
plt.show()


#Solving the IQR fro avg price room
quartiles = np.quantile(data['avg_price_per_room'][data['avg_price_per_room'].notnull()], [.25, .75])
power_4iqr = 4 * (quartiles[1] - quartiles[0])
print(f'Q1 = {quartiles[0]}, Q3 = {quartiles[1]}, 4*IQR = {power_4iqr}')
outlier_powers = data.loc[np.abs(data['avg_price_per_room'] - data['avg_price_per_room'].median()) > power_4iqr, 'avg_price_per_room']
outlier_powers.shape

Q1 = 80.3, Q3 = 120.0, 4*IQR = 158.8

(49,)


# creating a list of columns
dist_cols = [
    item for item in data.select_dtypes(include=np.number).columns
]

plt.figure(figsize=(15, 45))
#looping the list and ploting histograms
for i in range(len(dist_cols)):
    plt.subplot(12, 3, i + 1)
    plt.hist(data[dist_cols[i]], bins=50)
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=15)

plt.show()


data2 = data.copy()

# Print dist_cols to see its contents
print("Contents of dist_cols:", dist_cols)

# List of columns to be removed
columns_to_remove = [
    'no_of_week_nights', 'no_of_adults', 'length_stay', 'avg_price_per_room',
    'arrival_year', 'required_car_parking_space', 'arrival_date',
    'arrival_month', 'repeated_guest', 'lead_time'
]

# Safely remove columns if they exist in dist_cols
for col in columns_to_remove:
    if col in dist_cols:
        dist_cols.remove(col)

# Print updated dist_cols to confirm removal
print("Updated dist_cols:", dist_cols)

Contents of dist_cols: ['no_of_children', 'no_of_weekend_nights', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'no_of_special_requests']
Updated dist_cols: ['no_of_children', 'no_of_weekend_nights', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'no_of_special_requests']


# using log transforms on some columns
for col in dist_cols:
    data2[col + "_log"] = np.log(data2[col] + 1)

# dropping the original columns
data2.drop(dist_cols, axis=1, inplace=True)
data2.head()


# Drop booking id
data2 = data2.drop('Booking_ID', axis=1)


data2.head()


# viewing the distributions after the log transformation.
dist_cols = [
    item for item in data2.select_dtypes(include=np.number).columns
]

#  plot histogram of all numeric columns

plt.figure(figsize=(15, 45))

for i in range(len(dist_cols)):
    plt.subplot(12, 3, i + 1)
    plt.hist(data2[dist_cols[i]], bins=50)  # Adjust the number of bins as needed
    sns.histplot(data=data2, x=dist_cols[i], kde=True)
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=25)

plt.show()


# OneHotEncoding catergorical variables
dummy_data = pd.get_dummies (
    data2,
    columns = [
        'type_of_meal_plan',
        'room_type_reserved',
        'market_segment_type',
    ],
    drop_first=True,
)
dummy_data.head()


dummy_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 28 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  int64  
 1   no_of_week_nights                         36275 non-null  int64  
 2   required_car_parking_space                36275 non-null  int64  
 3   lead_time                                 36275 non-null  int64  
 4   arrival_year                              36275 non-null  int64  
 5   arrival_month                             36275 non-null  int64  
 6   arrival_date                              36275 non-null  int64  
 7   repeated_guest                            36275 non-null  int64  
 8   avg_price_per_room                        36275 non-null  float64
 9   booking_status                            36275 non-null  object 
 10  no_of_children_log                        36275 non-null  float64
 11  no_of_weekend_nights_log                  36275 non-null  float64
 12  no_of_previous_cancellations_log          36275 non-null  float64
 13  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 14  no_of_special_requests_log                36275 non-null  float64
 15  type_of_meal_plan_Meal Plan 2             36275 non-null  bool   
 16  type_of_meal_plan_Meal Plan 3             36275 non-null  bool   
 17  type_of_meal_plan_Not Selected            36275 non-null  bool   
 18  room_type_reserved_Room_Type 2            36275 non-null  bool   
 19  room_type_reserved_Room_Type 3            36275 non-null  bool   
 20  room_type_reserved_Room_Type 4            36275 non-null  bool   
 21  room_type_reserved_Room_Type 5            36275 non-null  bool   
 22  room_type_reserved_Room_Type 6            36275 non-null  bool   
 23  room_type_reserved_Room_Type 7            36275 non-null  bool   
 24  market_segment_type_Complementary         36275 non-null  bool   
 25  market_segment_type_Corporate             36275 non-null  bool   
 26  market_segment_type_Offline               36275 non-null  bool   
 27  market_segment_type_Online                36275 non-null  bool   
dtypes: bool(13), float64(6), int64(8), object(1)
memory usage: 4.6+ MB


dummied_cut = pd.cut(dummy_data['lead_time'], 5, labels=['lat_min','short','med','long','advanced'])
dummied_cut.head(10)


data3 = pd.merge(dummy_data, dummied_cut, left_index=True, right_index=True)

data3.head().T


# dropping time variables and lead_time_x since it has been binned into 5 columns.
data3_5 = data3.drop(['lead_time_x','arrival_date', 'arrival_year'], axis=1)


data4 = pd.get_dummies (
    data3_5,
    columns = [
        'lead_time_y',
    ],
    drop_first=True,
)
data4.head().T


data4 = data4.astype(float)


data4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 29 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  float64
 1   no_of_week_nights                         36275 non-null  float64
 2   required_car_parking_space                36275 non-null  float64
 3   arrival_month                             36275 non-null  float64
 4   repeated_guest                            36275 non-null  float64
 5   avg_price_per_room                        36275 non-null  float64
 6   booking_status                            36275 non-null  float64
 7   no_of_children_log                        36275 non-null  float64
 8   no_of_weekend_nights_log                  36275 non-null  float64
 9   no_of_previous_cancellations_log          36275 non-null  float64
 10  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 11  no_of_special_requests_log                36275 non-null  float64
 12  type_of_meal_plan_Meal Plan 2             36275 non-null  float64
 13  type_of_meal_plan_Meal Plan 3             36275 non-null  float64
 14  type_of_meal_plan_Not Selected            36275 non-null  float64
 15  room_type_reserved_Room_Type 2            36275 non-null  float64
 16  room_type_reserved_Room_Type 3            36275 non-null  float64
 17  room_type_reserved_Room_Type 4            36275 non-null  float64
 18  room_type_reserved_Room_Type 5            36275 non-null  float64
 19  room_type_reserved_Room_Type 6            36275 non-null  float64
 20  room_type_reserved_Room_Type 7            36275 non-null  float64
 21  market_segment_type_Complementary         36275 non-null  float64
 22  market_segment_type_Corporate             36275 non-null  float64
 23  market_segment_type_Offline               36275 non-null  float64
 24  market_segment_type_Online                36275 non-null  float64
 25  lead_time_y_short                         36275 non-null  float64
 26  lead_time_y_med                           36275 non-null  float64
 27  lead_time_y_long                          36275 non-null  float64
 28  lead_time_y_advanced                      36275 non-null  float64
dtypes: float64(29)
memory usage: 8.0 MB


# Assuming data has the original 'booking_status' column
if 'booking_status' in data.columns:
    data4['booking_status'] = data['booking_status']
else:
    print("'booking_status' not found in the original data")


print(data4.columns)

Index(['no_of_adults', 'no_of_week_nights', 'required_car_parking_space',
       'arrival_month', 'repeated_guest', 'avg_price_per_room',
       'no_of_children_log', 'no_of_weekend_nights_log',
       'no_of_previous_cancellations_log',
       'no_of_previous_bookings_not_canceled_log',
       'no_of_special_requests_log', 'type_of_meal_plan_Meal Plan 2',
       'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected',
       'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3',
       'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5',
       'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7',
       'market_segment_type_Complementary', 'market_segment_type_Corporate',
       'market_segment_type_Offline', 'market_segment_type_Online',
       'lead_time_y_short', 'lead_time_y_med', 'lead_time_y_long',
       'lead_time_y_advanced', 'booking_status'],
      dtype='object')


# Using the SCIEM method I will split the train test data first.
X = data4.drop("booking_status" , axis=1)
y = data4.pop("booking_status")


# Import add_constant from statsmodels
from statsmodels.api import add_constant

# Adding a constant (intercept) column to X
X = add_constant(X)

# Now, X has a constant column added
print(X.head())

   const  no_of_adults  no_of_week_nights  required_car_parking_space  \
0    1.0           2.0                2.0                         0.0   
1    1.0           2.0                3.0                         0.0   
2    1.0           1.0                1.0                         0.0   
3    1.0           2.0                2.0                         0.0   
4    1.0           2.0                1.0                         0.0   

   arrival_month  repeated_guest  avg_price_per_room  no_of_children_log  \
0           10.0             0.0               65.00                 0.0   
1           11.0             0.0              106.68                 0.0   
2            2.0             0.0               60.00                 0.0   
3            5.0             0.0              100.00                 0.0   
4            4.0             0.0               94.50                 0.0   

   no_of_weekend_nights_log  no_of_previous_cancellations_log  ...  \
0                  0.693147                               0.0  ...   
1                  1.098612                               0.0  ...   
2                  1.098612                               0.0  ...   
3                  0.000000                               0.0  ...   
4                  0.693147                               0.0  ...   

   room_type_reserved_Room_Type 6  room_type_reserved_Room_Type 7  \
0                             0.0                             0.0   
1                             0.0                             0.0   
2                             0.0                             0.0   
3                             0.0                             0.0   
4                             0.0                             0.0   

   market_segment_type_Complementary  market_segment_type_Corporate  \
0                                0.0                            0.0   
1                                0.0                            0.0   
2                                0.0                            0.0   
3                                0.0                            0.0   
4                                0.0                            0.0   

   market_segment_type_Offline  market_segment_type_Online  lead_time_y_short  \
0                          1.0                         0.0                0.0   
1                          0.0                         1.0                0.0   
2                          0.0                         1.0                0.0   
3                          0.0                         1.0                0.0   
4                          0.0                         1.0                0.0   

   lead_time_y_med  lead_time_y_long  lead_time_y_advanced  
0              1.0               0.0                   0.0  
1              0.0               0.0                   0.0  
2              0.0               0.0                   0.0  
3              1.0               0.0                   0.0  
4              0.0               0.0                   0.0  

[5 rows x 29 columns]


# Train/Test Split 70/30
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)


print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 25392
Number of rows in test data = 10883


print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Percentage of classes in training set:
booking_status
0.0    0.670644
1.0    0.329356
Name: proportion, dtype: float64
Percentage of classes in test set:
booking_status
0.0    0.676376
1.0    0.323624
Name: proportion, dtype: float64


X_train.info()

<class 'pandas.core.frame.DataFrame'>
Index: 25392 entries, 13662 to 33003
Data columns (total 28 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              25392 non-null  float64
 1   no_of_week_nights                         25392 non-null  float64
 2   required_car_parking_space                25392 non-null  float64
 3   arrival_month                             25392 non-null  float64
 4   repeated_guest                            25392 non-null  float64
 5   avg_price_per_room                        25392 non-null  float64
 6   no_of_children_log                        25392 non-null  float64
 7   no_of_weekend_nights_log                  25392 non-null  float64
 8   no_of_previous_cancellations_log          25392 non-null  float64
 9   no_of_previous_bookings_not_canceled_log  25392 non-null  float64
 10  no_of_special_requests_log                25392 non-null  float64
 11  type_of_meal_plan_Meal Plan 2             25392 non-null  float64
 12  type_of_meal_plan_Meal Plan 3             25392 non-null  float64
 13  type_of_meal_plan_Not Selected            25392 non-null  float64
 14  room_type_reserved_Room_Type 2            25392 non-null  float64
 15  room_type_reserved_Room_Type 3            25392 non-null  float64
 16  room_type_reserved_Room_Type 4            25392 non-null  float64
 17  room_type_reserved_Room_Type 5            25392 non-null  float64
 18  room_type_reserved_Room_Type 6            25392 non-null  float64
 19  room_type_reserved_Room_Type 7            25392 non-null  float64
 20  market_segment_type_Complementary         25392 non-null  float64
 21  market_segment_type_Corporate             25392 non-null  float64
 22  market_segment_type_Offline               25392 non-null  float64
 23  market_segment_type_Online                25392 non-null  float64
 24  lead_time_y_short                         25392 non-null  float64
 25  lead_time_y_med                           25392 non-null  float64
 26  lead_time_y_long                          25392 non-null  float64
 27  lead_time_y_advanced                      25392 non-null  float64
dtypes: float64(28)
memory usage: 5.6 MB


# It is a good idea to explore the data once again after manipulating it.
plt.figure(figsize=(20,10))
sns.heatmap(
data4.corr(), annot=True, vmin=-1, vmax=1, fmt='.2f')

<Axes: >


import statsmodels.api as sm
from statsmodels.api import add_constant
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd


# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

no_of_adults                                18.197098
no_of_week_nights                            3.680150
required_car_parking_space                   1.075494
arrival_month                                7.097404
repeated_guest                               3.417968
avg_price_per_room                          17.989022
no_of_children_log                           2.006153
no_of_weekend_nights_log                     2.133807
no_of_previous_cancellations_log             1.608889
no_of_previous_bookings_not_canceled_log     3.570483
no_of_special_requests_log                   2.202569
type_of_meal_plan_Meal Plan 2                1.334174
type_of_meal_plan_Meal Plan 3                1.025507
type_of_meal_plan_Not Selected               1.432984
room_type_reserved_Room_Type 2               1.111269
room_type_reserved_Room_Type 3               1.003573
room_type_reserved_Room_Type 4               1.638586
room_type_reserved_Room_Type 5               1.034873
room_type_reserved_Room_Type 6               1.903476
room_type_reserved_Room_Type 7               1.114383
market_segment_type_Complementary            1.296787
market_segment_type_Corporate                2.418865
market_segment_type_Offline                  9.385691
market_segment_type_Online                  23.630149
lead_time_y_short                            1.424498
lead_time_y_med                              1.218744
lead_time_y_long                             1.207261
lead_time_y_advanced                         1.054674
dtype: float64


#dropping the number of weekend & week nights because I have combined them into one & market segements because they all have large multi values
X_train1 = X_train.drop(['no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary'],
                       axis=1)


# Import necessary libraries
import statsmodels.api as sm
from statsmodels.api import add_constant


logit = sm.Logit(y_train, X_train1.astype(float))
lg = logit.fit()

Optimization terminated successfully.
         Current function value: 0.495874
         Iterations 10


# print the logistic regression summary
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25370
Method:                           MLE   Df Model:                           21
Date:                Sun, 08 Sep 2024   Pseudo R-squ.:                  0.2175
Time:                        16:03:56   Log-Likelihood:                -12591.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
============================================================================================================
                                               coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------------
no_of_adults                                -0.4712      0.028    -17.051      0.000      -0.525      -0.417
required_car_parking_space                  -1.3018      0.130     -9.990      0.000      -1.557      -1.046
arrival_month                               -0.1376      0.005    -27.464      0.000      -0.147      -0.128
repeated_guest                              -3.2515      0.644     -5.050      0.000      -4.513      -1.990
avg_price_per_room                           0.0086      0.001     16.833      0.000       0.008       0.010
no_of_children_log                           0.5743      0.091      6.341      0.000       0.397       0.752
no_of_previous_cancellations_log             1.2858      0.459      2.799      0.005       0.385       2.186
no_of_previous_bookings_not_canceled_log    -1.0502      0.527     -1.993      0.046      -2.083      -0.017
no_of_special_requests_log                  -1.6744      0.041    -40.640      0.000      -1.755      -1.594
type_of_meal_plan_Meal Plan 2               -0.2106      0.055     -3.841      0.000      -0.318      -0.103
type_of_meal_plan_Meal Plan 3                0.5421      1.286      0.422      0.673      -1.978       3.062
type_of_meal_plan_Not Selected               0.5950      0.046     12.887      0.000       0.504       0.685
room_type_reserved_Room_Type 2              -0.3656      0.119     -3.074      0.002      -0.599      -0.132
room_type_reserved_Room_Type 3              -0.3770      0.987     -0.382      0.703      -2.312       1.558
room_type_reserved_Room_Type 4               0.5761      0.046     12.576      0.000       0.486       0.666
room_type_reserved_Room_Type 5              -0.6209      0.192     -3.228      0.001      -0.998      -0.244
room_type_reserved_Room_Type 6               0.0997      0.127      0.783      0.434      -0.150       0.349
room_type_reserved_Room_Type 7              -0.0272      0.264     -0.103      0.918      -0.545       0.490
lead_time_y_short                            1.1559      0.037     31.123      0.000       1.083       1.229
lead_time_y_med                              2.7193      0.057     47.914      0.000       2.608       2.831
lead_time_y_long                             2.8778      0.076     37.914      0.000       2.729       3.027
lead_time_y_advanced                         4.3622      0.248     17.618      0.000       3.877       4.847
============================================================================================================


# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train1.values, i) for i in range(X_train1.shape[1])],
    index=X_train1.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

no_of_adults                                11.519816
required_car_parking_space                   1.069077
arrival_month                                5.913945
repeated_guest                               3.260798
avg_price_per_room                          11.718683
no_of_children_log                           1.991388
no_of_previous_cancellations_log             1.587147
no_of_previous_bookings_not_canceled_log     3.488538
no_of_special_requests_log                   1.961361
type_of_meal_plan_Meal Plan 2                1.243078
type_of_meal_plan_Meal Plan 3                1.017756
type_of_meal_plan_Not Selected               1.276209
room_type_reserved_Room_Type 2               1.089233
room_type_reserved_Room_Type 3               1.000940
room_type_reserved_Room_Type 4               1.494463
room_type_reserved_Room_Type 5               1.020678
room_type_reserved_Room_Type 6               1.823130
room_type_reserved_Room_Type 7               1.065028
lead_time_y_short                            1.372970
lead_time_y_med                              1.192447
lead_time_y_long                             1.179291
lead_time_y_advanced                         1.051144
dtype: float64


# test performance
pred_train = lg.predict(X_train1) > 0.5
pred_train = np.round(pred_train)


X_train2 = X_train1.drop(['room_type_reserved_Room_Type 3'], axis=1)
X_train2.info()

<class 'pandas.core.frame.DataFrame'>
Index: 25392 entries, 13662 to 33003
Data columns (total 21 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              25392 non-null  float64
 1   required_car_parking_space                25392 non-null  float64
 2   arrival_month                             25392 non-null  float64
 3   repeated_guest                            25392 non-null  float64
 4   avg_price_per_room                        25392 non-null  float64
 5   no_of_children_log                        25392 non-null  float64
 6   no_of_previous_cancellations_log          25392 non-null  float64
 7   no_of_previous_bookings_not_canceled_log  25392 non-null  float64
 8   no_of_special_requests_log                25392 non-null  float64
 9   type_of_meal_plan_Meal Plan 2             25392 non-null  float64
 10  type_of_meal_plan_Meal Plan 3             25392 non-null  float64
 11  type_of_meal_plan_Not Selected            25392 non-null  float64
 12  room_type_reserved_Room_Type 2            25392 non-null  float64
 13  room_type_reserved_Room_Type 4            25392 non-null  float64
 14  room_type_reserved_Room_Type 5            25392 non-null  float64
 15  room_type_reserved_Room_Type 6            25392 non-null  float64
 16  room_type_reserved_Room_Type 7            25392 non-null  float64
 17  lead_time_y_short                         25392 non-null  float64
 18  lead_time_y_med                           25392 non-null  float64
 19  lead_time_y_long                          25392 non-null  float64
 20  lead_time_y_advanced                      25392 non-null  float64
dtypes: float64(21)
memory usage: 4.3 MB


logit = sm.Logit(y_train, X_train2.astype(float))
lg2 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.495877
         Iterations 10


print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25371
Method:                           MLE   Df Model:                           20
Date:                Sun, 08 Sep 2024   Pseudo R-squ.:                  0.2175
Time:                        16:07:42   Log-Likelihood:                -12591.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
============================================================================================================
                                               coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------------
no_of_adults                                -0.4712      0.028    -17.051      0.000      -0.525      -0.417
required_car_parking_space                  -1.3018      0.130     -9.990      0.000      -1.557      -1.046
arrival_month                               -0.1376      0.005    -27.477      0.000      -0.147      -0.128
repeated_guest                              -3.2514      0.644     -5.050      0.000      -4.513      -1.989
avg_price_per_room                           0.0086      0.001     16.836      0.000       0.008       0.010
no_of_children_log                           0.5743      0.091      6.341      0.000       0.397       0.752
no_of_previous_cancellations_log             1.2857      0.459      2.799      0.005       0.385       2.186
no_of_previous_bookings_not_canceled_log    -1.0503      0.527     -1.993      0.046      -2.083      -0.017
no_of_special_requests_log                  -1.6742      0.041    -40.638      0.000      -1.755      -1.593
type_of_meal_plan_Meal Plan 2               -0.2105      0.055     -3.839      0.000      -0.318      -0.103
type_of_meal_plan_Meal Plan 3                0.5422      1.286      0.422      0.673      -1.978       3.062
type_of_meal_plan_Not Selected               0.5949      0.046     12.887      0.000       0.504       0.685
room_type_reserved_Room_Type 2              -0.3655      0.119     -3.073      0.002      -0.599      -0.132
room_type_reserved_Room_Type 4               0.5762      0.046     12.577      0.000       0.486       0.666
room_type_reserved_Room_Type 5              -0.6208      0.192     -3.228      0.001      -0.998      -0.244
room_type_reserved_Room_Type 6               0.0996      0.127      0.782      0.434      -0.150       0.349
room_type_reserved_Room_Type 7              -0.0273      0.264     -0.103      0.918      -0.545       0.490
lead_time_y_short                            1.1558      0.037     31.121      0.000       1.083       1.229
lead_time_y_med                              2.7193      0.057     47.913      0.000       2.608       2.831
lead_time_y_long                             2.8780      0.076     37.917      0.000       2.729       3.027
lead_time_y_advanced                         4.3622      0.248     17.618      0.000       3.877       4.848
============================================================================================================


X_train3 = X_train2.drop(['no_of_previous_bookings_not_canceled_log'], axis=1)


logit = sm.Logit(y_train, X_train3.astype(float))
lg3 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.495990
         Iterations 9


print(lg3.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25372
Method:                           MLE   Df Model:                           19
Date:                Sun, 08 Sep 2024   Pseudo R-squ.:                  0.2173
Time:                        16:08:29   Log-Likelihood:                -12594.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
no_of_adults                        -0.4715      0.028    -17.060      0.000      -0.526      -0.417
required_car_parking_space          -1.3007      0.130     -9.983      0.000      -1.556      -1.045
arrival_month                       -0.1376      0.005    -27.482      0.000      -0.147      -0.128
repeated_guest                      -3.9896      0.551     -7.239      0.000      -5.070      -2.909
avg_price_per_room                   0.0086      0.001     16.848      0.000       0.008       0.010
no_of_children_log                   0.5749      0.091      6.348      0.000       0.397       0.752
no_of_previous_cancellations_log     1.0029      0.376      2.666      0.008       0.265       1.740
no_of_special_requests_log          -1.6758      0.041    -40.677      0.000      -1.757      -1.595
type_of_meal_plan_Meal Plan 2       -0.2122      0.055     -3.869      0.000      -0.320      -0.105
type_of_meal_plan_Meal Plan 3        0.5422      1.285      0.422      0.673      -1.977       3.062
type_of_meal_plan_Not Selected       0.5952      0.046     12.892      0.000       0.505       0.686
room_type_reserved_Room_Type 2      -0.3661      0.119     -3.077      0.002      -0.599      -0.133
room_type_reserved_Room_Type 4       0.5764      0.046     12.581      0.000       0.487       0.666
room_type_reserved_Room_Type 5      -0.6225      0.192     -3.238      0.001      -0.999      -0.246
room_type_reserved_Room_Type 6       0.0989      0.127      0.777      0.437      -0.151       0.348
room_type_reserved_Room_Type 7      -0.0296      0.264     -0.112      0.911      -0.547       0.488
lead_time_y_short                    1.1561      0.037     31.129      0.000       1.083       1.229
lead_time_y_med                      2.7213      0.057     47.943      0.000       2.610       2.833
lead_time_y_long                     2.8798      0.076     37.924      0.000       2.731       3.029
lead_time_y_advanced                 4.4037      0.250     17.620      0.000       3.914       4.894
====================================================================================================


# let's check the VIF of the predictors again to see if any Multicollinearity persist
vif_series = pd.Series(
    [variance_inflation_factor(X_train3.values, i) for i in range(X_train3.shape[1])],
    index=X_train3.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

no_of_adults                        11.519270
required_car_parking_space           1.068324
arrival_month                        5.911260
repeated_guest                       1.508209
avg_price_per_room                  11.712940
no_of_children_log                   1.991288
no_of_previous_cancellations_log     1.436736
no_of_special_requests_log           1.952941
type_of_meal_plan_Meal Plan 2        1.242879
type_of_meal_plan_Meal Plan 3        1.017726
type_of_meal_plan_Not Selected       1.276122
room_type_reserved_Room_Type 2       1.089226
room_type_reserved_Room_Type 4       1.494420
room_type_reserved_Room_Type 5       1.019830
room_type_reserved_Room_Type 6       1.822871
room_type_reserved_Room_Type 7       1.064638
lead_time_y_short                    1.372770
lead_time_y_med                      1.192427
lead_time_y_long                     1.179198
lead_time_y_advanced                 1.050944
dtype: float64


X_train4 = X_train3.drop(['room_type_reserved_Room_Type 2'], axis=1)

logit = sm.Logit(y_train, X_train4.astype(float))
lg4 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.496180
         Iterations 9


print(lg4.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25373
Method:                           MLE   Df Model:                           18
Date:                Sun, 08 Sep 2024   Pseudo R-squ.:                  0.2170
Time:                        16:09:27   Log-Likelihood:                -12599.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
no_of_adults                        -0.4727      0.028    -17.118      0.000      -0.527      -0.419
required_car_parking_space          -1.3070      0.130    -10.021      0.000      -1.563      -1.051
arrival_month                       -0.1376      0.005    -27.504      0.000      -0.147      -0.128
repeated_guest                      -3.9887      0.551     -7.239      0.000      -5.069      -2.909
avg_price_per_room                   0.0086      0.001     16.834      0.000       0.008       0.010
no_of_children_log                   0.5073      0.088      5.771      0.000       0.335       0.680
no_of_previous_cancellations_log     1.0050      0.376      2.674      0.008       0.268       1.742
no_of_special_requests_log          -1.6788      0.041    -40.761      0.000      -1.760      -1.598
type_of_meal_plan_Meal Plan 2       -0.2055      0.055     -3.750      0.000      -0.313      -0.098
type_of_meal_plan_Meal Plan 3        0.5230      1.282      0.408      0.683      -1.991       3.037
type_of_meal_plan_Not Selected       0.6001      0.046     13.005      0.000       0.510       0.691
room_type_reserved_Room_Type 4       0.5847      0.046     12.782      0.000       0.495       0.674
room_type_reserved_Room_Type 5      -0.6109      0.192     -3.180      0.001      -0.987      -0.234
room_type_reserved_Room_Type 6       0.1712      0.125      1.369      0.171      -0.074       0.416
room_type_reserved_Room_Type 7       0.0189      0.263      0.072      0.943      -0.496       0.534
lead_time_y_short                    1.1533      0.037     31.065      0.000       1.080       1.226
lead_time_y_med                      2.7090      0.057     47.907      0.000       2.598       2.820
lead_time_y_long                     2.8751      0.076     37.901      0.000       2.726       3.024
lead_time_y_advanced                 4.3985      0.250     17.604      0.000       3.909       4.888
====================================================================================================


X_train5 = X_train4.drop(['room_type_reserved_Room_Type 4'], axis=1)

logit = sm.Logit(y_train, X_train5.astype(float))
lg5 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.499373
         Iterations 9


print(lg5.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25374
Method:                           MLE   Df Model:                           17
Date:                Sun, 08 Sep 2024   Pseudo R-squ.:                  0.2120
Time:                        16:10:07   Log-Likelihood:                -12680.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
no_of_adults                        -0.4324      0.027    -15.812      0.000      -0.486      -0.379
required_car_parking_space          -1.2989      0.130    -10.009      0.000      -1.553      -1.045
arrival_month                       -0.1445      0.005    -29.008      0.000      -0.154      -0.135
repeated_guest                      -4.0168      0.550     -7.300      0.000      -5.095      -2.938
avg_price_per_room                   0.0099      0.001     19.711      0.000       0.009       0.011
no_of_children_log                   0.4024      0.087      4.632      0.000       0.232       0.573
no_of_previous_cancellations_log     0.9906      0.377      2.629      0.009       0.252       1.729
no_of_special_requests_log          -1.6350      0.041    -40.101      0.000      -1.715      -1.555
type_of_meal_plan_Meal Plan 2       -0.3010      0.054     -5.548      0.000      -0.407      -0.195
type_of_meal_plan_Meal Plan 3        0.5454      1.349      0.404      0.686      -2.098       3.189
type_of_meal_plan_Not Selected       0.4520      0.044     10.200      0.000       0.365       0.539
room_type_reserved_Room_Type 5      -0.7607      0.191     -3.988      0.000      -1.135      -0.387
room_type_reserved_Room_Type 6      -0.0001      0.123     -0.001      0.999      -0.242       0.242
room_type_reserved_Room_Type 7      -0.2404      0.261     -0.922      0.356      -0.751       0.270
lead_time_y_short                    1.1233      0.037     30.394      0.000       1.051       1.196
lead_time_y_med                      2.6371      0.056     47.049      0.000       2.527       2.747
lead_time_y_long                     2.7832      0.075     36.876      0.000       2.635       2.931
lead_time_y_advanced                 4.3071      0.250     17.242      0.000       3.817       4.797
====================================================================================================


# converting coefficients to odds
odds = np.exp(lg5.params)

# adding the odds to a dataframe
pd.DataFrame(odds, X_train5.columns, columns=["odds"]).T


# finding the percentage change
perc_change_odds = (np.exp(lg5.params) - 1) * 100

# adding the change_odds% to a dataframe
pd.DataFrame(perc_change_odds, X_train3.columns, columns=["change_odds%"]).T


# fitting the model on training set
logit = sm.Logit(y_train, X_train5.astype(float))
lg3 = logit.fit()

pred_train4 = lg5.predict(X_train5)
pred_train4 = np.round(pred_train4)

Optimization terminated successfully.
         Current function value: 0.499373
         Iterations 9


# another confusion matrix
cm = confusion_matrix(y_train, pred_train4)
plt.figure(figsize=(7, 5))
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()


print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))

Accuracy on training set :  0.7559861373660995


logit_roc_auc_train = roc_auc_score(y_train, lg5.predict(X_train5))
fpr, tpr, thresholds = roc_curve(y_train, lg5.predict(X_train5))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# dropping variables from test set as well which were dropped from training set
X_test1 = X_test.drop([ 'no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary',
                       'room_type_reserved_Room_Type 3',
                       'room_type_reserved_Room_Type 4',
                       'no_of_previous_bookings_not_canceled_log',
                       'room_type_reserved_Room_Type 2'

                     ], axis=1)


pred_test = lg5.predict(X_test1) > 0.5
pred_test = np.round(pred_test)
print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))
print("Accuracy on test set : ", accuracy_score(y_test, pred_test))

Accuracy on training set :  0.7559861373660995
Accuracy on test set :  0.7647707433612055


tree_data = dummy_data.astype(float)

tree_data = tree_data.drop(['arrival_date','arrival_year','no_of_week_nights',
'no_of_weekend_nights_log'  ], axis=1)

X = tree_data.drop("booking_status" , axis=1)
y = tree_data.pop("booking_status")

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)


# building a decision tree using the dtclassifier function
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
dTree.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)


#scoring the accuracy on train & test data
print("Accuracy on training set : ",dTree.score(X_train, y_train))
print("Accuracy on test set : ",dTree.score(X_test, y_test))

Accuracy on training set :  0.9884215500945179
Accuracy on test set :  0.8645594045759442


# checking the positive outcomes
y.sum(axis = 0)

11885.0


## Function to create confusion matrix
def make_confusion_matrix(model,y_actual,labels=[1, 0]):
    '''
    model : classifier to predict values of X
    y_actual : ground truth

    '''
    y_predict = model.predict(X_test)
    cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                  columns = [i for i in ['Predicted - No','Predicted - Yes']])
    group_counts = ["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                         cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
              zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


##  Function to calculate recall score
def get_recall_score(model):
    '''
    model : classifier to predict values of X

    '''
    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)
    print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
    print("Recall on test set : ",metrics.recall_score(y_test,pred_test))


# Import necessary libraries
from sklearn.metrics import confusion_matrix, accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np


# Define a function to make the confusion matrix
def make_confusion_matrix(model, y_test, X_test):
    # Generate predictions using the model
    y_pred = model.predict(X_test)

    # Create the confusion matrix
    cm = confusion_matrix(y_test, y_pred)

    # Plot the confusion matrix
    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['False', 'True'], yticklabels=['False', 'True'])
    plt.ylabel('Actual')
    plt.xlabel('Predicted')
    plt.title('Confusion Matrix')
    plt.show()

# Assuming you have a trained decision tree model `dTree`
# Now call the function to display the confusion matrix
make_confusion_matrix(dTree, y_test, X_test)


# Import necessary library
from sklearn.metrics import recall_score

# Define a function to calculate recall score on train and test data
def get_recall_score(model, X_train, y_train, X_test, y_test):
    # Get predictions on both training and test sets
    y_train_pred = model.predict(X_train)
    y_test_pred = model.predict(X_test)

    # Calculate recall score on training set
    recall_train = recall_score(y_train, y_train_pred)

    # Calculate recall score on test set
    recall_test = recall_score(y_test, y_test_pred)

    # Print the recall scores
    print(f"Recall Score on Training Set: {recall_train:.4f}")
    print(f"Recall Score on Test Set: {recall_test:.4f}")

# Assuming you have a trained decision tree model `dTree` and train/test data
get_recall_score(dTree, X_train, y_train, X_test, y_test)

Recall Score on Training Set: 0.9711
Recall Score on Test Set: 0.7978


the_features = list(X.columns)
print(the_features)

['no_of_adults', 'required_car_parking_space', 'lead_time', 'arrival_month', 'repeated_guest', 'avg_price_per_room', 'no_of_children_log', 'no_of_previous_cancellations_log', 'no_of_previous_bookings_not_canceled_log', 'no_of_special_requests_log', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']


# Import necessary libraries
import matplotlib.pyplot as plt
from sklearn.tree import plot_tree


# checking out what variables are being prioritized by the model.

print (pd.DataFrame(dTree.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                               Imp
lead_time                                 0.428752
avg_price_per_room                        0.229776
market_segment_type_Online                0.093703
arrival_month                             0.089176
no_of_special_requests_log                0.069752
no_of_adults                              0.033601
type_of_meal_plan_Not Selected            0.011043
room_type_reserved_Room_Type 4            0.010264
required_car_parking_space                0.007843
no_of_children_log                        0.007265
type_of_meal_plan_Meal Plan 2             0.005708
market_segment_type_Offline               0.004476
room_type_reserved_Room_Type 2            0.002571
room_type_reserved_Room_Type 5            0.001512
room_type_reserved_Room_Type 6            0.001388
market_segment_type_Corporate             0.001179
no_of_previous_bookings_not_canceled_log  0.000707
room_type_reserved_Room_Type 7            0.000500
repeated_guest                            0.000433
no_of_previous_cancellations_log          0.000339
room_type_reserved_Room_Type 3            0.000013
market_segment_type_Complementary         0.000000
type_of_meal_plan_Meal Plan 3             0.000000


importances = dTree.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


# Pre prune the model with max depth hyperparameter
dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
dTree1.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)


# Define the confusion matrix function
def make_confusion_matrix(model, X_test, y_test):
    # Generate predictions using the model
    y_pred = model.predict(X_test)

    # Create the confusion matrix
    cm = confusion_matrix(y_test, y_pred)

    # Plot the confusion matrix using seaborn
    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['False', 'True'], yticklabels=['False', 'True'])
    plt.ylabel('Actual')
    plt.xlabel('Predicted')
    plt.title('Confusion Matrix')
    plt.show()

# Call the function with the correct arguments
make_confusion_matrix(dTree1, X_test, y_test)  # Ensure X_test is passed along with y_test


# The accuracy on the pre pruned tree.
print("Accuracy on training set : ",dTree1.score(X_train, y_train))
print("Accuracy on test set : ",dTree1.score(X_test, y_test))

Accuracy on training set :  0.7844202898550725
Accuracy on test set :  0.7913259211614444


# Looking at the feature importances of this model
importances = dTree1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


# Import necessary libraries
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer, recall_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns


# Choose the type of classifier
estimator = DecisionTreeClassifier(random_state=1)

# Reduce the parameter grid to fewer combinations
parameters = {
    'max_depth': np.arange(3, 8),  # Narrowed the range to reduce combinations
    'min_samples_leaf': [1, 5, 10],  # Fewer values
    'max_leaf_nodes': [5, 10],  # Reduced options
    'min_impurity_decrease': [0.001, 0.01]  # Simplified grid
}

# Scoring function used to compare parameter combinations (recall score)
acc_scorer = make_scorer(recall_score)

# Run the grid search with reduced cv and parallel processing
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=3, n_jobs=-1)  # Use 3-fold CV and parallelization
grid_obj = grid_obj.fit(X_train, y_train)

# Set the estimator to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data
estimator.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
                       min_impurity_decrease=0.001, random_state=1)

DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
                       min_impurity_decrease=0.001, random_state=1)


# Define the confusion matrix function (if not already defined)
def make_confusion_matrix(model, X_test, y_test):
    # Generate predictions using the model
    y_pred = model.predict(X_test)

    # Create the confusion matrix
    cm = confusion_matrix(y_test, y_pred)

    # Plot the confusion matrix using seaborn
    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['False', 'True'], yticklabels=['False', 'True'])
    plt.ylabel('Actual')
    plt.xlabel('Predicted')
    plt.title('Confusion Matrix')
    plt.show()

# Assuming `estimator` is your trained model and `X_test`, `y_test` are defined

# Call the function with both X_test and y_test as arguments
make_confusion_matrix(estimator, X_test, y_test)


# The accuracy on the estimator tree.
print("Accuracy on training set : ",estimator.score(X_train, y_train))
print("Accuracy on test set : ",estimator.score(X_test, y_test))

Accuracy on training set :  0.7694943289224953
Accuracy on test set :  0.7719378847744188


importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities


pd.DataFrame(path)


fig, ax = plt.subplots(figsize=(10,5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


# Import necessary libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
import numpy as np

# Assuming you have computed ccp_alphas using cost complexity pruning


# Limiting the number of ccp_alpha values (e.g., choose 10 evenly spaced alphas)
ccp_alphas = np.linspace(min(ccp_alphas), max(ccp_alphas), 10)

# List to store classifiers for each alpha
clfs = []

# Loop through the reduced list of alphas and train decision trees
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(X_train, y_train)
    clfs.append(clf)
    # Logging the progress for better monitoring
    print(f"Trained decision tree with ccp_alpha: {ccp_alpha}")

# Print the number of nodes in the last tree
print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
      clfs[-1].tree_.node_count, ccp_alphas[-1]))

Trained decision tree with ccp_alpha: 0.0
Trained decision tree with ccp_alpha: 0.008508654974857064
Trained decision tree with ccp_alpha: 0.017017309949714128
Trained decision tree with ccp_alpha: 0.025525964924571192
Trained decision tree with ccp_alpha: 0.034034619899428256
Trained decision tree with ccp_alpha: 0.04254327487428532
Trained decision tree with ccp_alpha: 0.051051929849142384
Trained decision tree with ccp_alpha: 0.05956058482399945
Trained decision tree with ccp_alpha: 0.06806923979885651
Trained decision tree with ccp_alpha: 0.07657789477371357
Number of nodes in the last tree is: 1 with ccp_alpha: 0.07657789477371357


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1,figsize=(10,7))
ax[0].plot(ccp_alphas, node_counts, marker='o', drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker='o', drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]


fig, ax = plt.subplots(figsize=(10,5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()


index_best_model = np.argmax(test_scores)
best_model = clfs[index_best_model]
print(best_model)
print('Training accuracy of best model: ',best_model.score(X_train, y_train))
print('Test accuracy of best model: ',best_model.score(X_test, y_test))

DecisionTreeClassifier(random_state=1)
Training accuracy of best model:  0.9884215500945179
Test accuracy of best model:  0.8645594045759442


from sklearn import metrics
from sklearn.metrics import recall_score

recall_train = []
for clf in clfs:
    pred_train3 = clf.predict(X_train)
    values_train = recall_score(y_train, pred_train3)
    recall_train.append(values_train)


recall_test=[]
for clf in clfs:
    pred_test3=clf.predict(X_test)
    values_test=metrics.recall_score(y_test,pred_test3)
    recall_test.append(values_test)


fig, ax = plt.subplots(figsize=(15,5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()


# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(random_state=1)


from sklearn.metrics import recall_score

# Recall score on training data
y_train_pred = best_model.predict(X_train)
recall_train = recall_score(y_train, y_train_pred)

# Recall score on test data
y_test_pred = best_model.predict(X_test)
recall_test = recall_score(y_test, y_test_pred)

print(f"Recall on training set: {recall_train}")
print(f"Recall on test set: {recall_test}")

Recall on training set: 0.9710630156642354
Recall on test set: 0.7978421351504826


# showing what metrics this model used
importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                         'Decision tree with post-pruning'], 'Train_Recall':[.981,.732,.732,.979], 'Test_Recall':[.792,.739,.739,.794]})
comparison_frame

	Month	No_of_Bookings
9	10	5317
8	9	4611
7	8	3813
5	6	3203
11	12	3021
10	11	2980
6	7	2920
3	4	2736
4	5	2598
2	3	2358
1	2	1704
0	1	1014

	ccp_alphas	impurities
0	0.000000	0.013864
1	0.000000	0.013864
2	0.000000	0.013864
3	0.000000	0.013864
4	0.000000	0.013864
...	...	...
1580	0.006666	0.286897
1581	0.013045	0.299942
1582	0.017260	0.317202
1583	0.023990	0.365183
1584	0.076578	0.441761

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	0.981	0.792
1	Decision tree with restricted maximum depth	0.732	0.739
2	Decision treee with hyperparameter tuning	0.732	0.739
3	Decision tree with post-pruning	0.979	0.794

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50	0	Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
36270	INN36271	3	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80	1	Not_Canceled
36271	INN36272	2	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95	2	Canceled
36272	INN36273	2	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39	2	Not_Canceled
36273	INN36274	2	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50	0	Canceled
36274	INN36275	2	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67	0	Not_Canceled

	0
Booking_ID	0
no_of_adults	0
no_of_children	0
no_of_weekend_nights	0
no_of_week_nights	0
type_of_meal_plan	0
required_car_parking_space	0
room_type_reserved	0
lead_time	0
arrival_year	0
arrival_month	0
arrival_date	0
market_segment_type	0
repeated_guest	0
no_of_previous_cancellations	0
no_of_previous_bookings_not_canceled	0
avg_price_per_room	0
no_of_special_requests	0
booking_status	0

	count	mean	std	min	25%	50%	75%	max
no_of_adults	36275.0	1.844962	0.518715	0.0	2.0	2.00	2.0	4.0
no_of_children	36275.0	0.105279	0.402648	0.0	0.0	0.00	0.0	10.0
no_of_weekend_nights	36275.0	0.810724	0.870644	0.0	0.0	1.00	2.0	7.0
no_of_week_nights	36275.0	2.204300	1.410905	0.0	1.0	2.00	3.0	17.0
required_car_parking_space	36275.0	0.030986	0.173281	0.0	0.0	0.00	0.0	1.0
lead_time	36275.0	85.232557	85.930817	0.0	17.0	57.00	126.0	443.0
arrival_year	36275.0	2017.820427	0.383836	2017.0	2018.0	2018.00	2018.0	2018.0
arrival_month	36275.0	7.423653	3.069894	1.0	5.0	8.00	10.0	12.0
arrival_date	36275.0	15.596995	8.740447	1.0	8.0	16.00	23.0	31.0
repeated_guest	36275.0	0.025637	0.158053	0.0	0.0	0.00	0.0	1.0
no_of_previous_cancellations	36275.0	0.023349	0.368331	0.0	0.0	0.00	0.0	13.0
no_of_previous_bookings_not_canceled	36275.0	0.153411	1.754171	0.0	0.0	0.00	0.0	58.0
avg_price_per_room	36275.0	103.423539	35.089424	0.0	80.3	99.45	120.0	540.0
no_of_special_requests	36275.0	0.619655	0.786236	0.0	0.0	0.00	1.0	5.0

	count
market_segment_type
Online	23214
Offline	10528
Corporate	2017
Complementary	391
Aviation	125

	no_of_adults	no_of_week_nights	lead_time	arrival_year	arrival_month	arrival_date	avg_price_per_room	booking_status	...	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online
0	2	2	224	2017	10	2	65.00	False	...	False	False	False	False	False	False	False	False	True	False
1	2	3	5	2018	11	6	106.68	False	...	False	False	False	False	False	False	False	False	False	True
2	1	1	1	2018	2	28	60.00	True	...	False	False	False	False	False	False	False	False	False	True
3	2	2	211	2018	5	20	100.00	True	...	False	False	False	False	False	False	False	False	False	True
4	2	1	48	2018	4	11	94.50	True	...	False	False	False	False	False	False	False	False	False	True

	lead_time
0	med
1	lat_min
2	lat_min
3	med
4	lat_min
5	long
6	lat_min
7	lat_min
8	short
9	lat_min

Problem statement¶

Data Description¶

Importing Libraries¶

Importing Data¶

Understanding the dataset¶

Exploratory Data Analysis¶

➡ Univariate Analysis¶

Booking_id¶

2. no_of_adults¶

3. no_of_children¶

4.no_of_weekend_nights¶

5.no_of_week_nights¶

6.type_of_meal_plan¶

7.required_car_parking_space¶

8.room_type_reserved¶

9.lead_time¶

10.arrival_year¶

11.arrival_month¶

12.arrival_date¶

13.market_segment_type¶

14.repeated_guest¶

15.no_of_previous_cancellations¶

16.no_of_previous_bookings_not_canceled¶

17.avg_price_per_room¶

18.no_of_special_requests¶

19.booking_status¶

➡ Bivariate Analysis¶

Number of Adults and Average Room Price¶

Number of Children and Average Room Price¶

Number of weekend nights and Room price¶

Lead time and Room price¶

Number of week nights and Room prices¶

Lead time and number of special requests¶

Number of special requests and room prices¶

Room price and arrival month¶

Room price and arrival date¶

Room price and repeated guests¶

Number of previous cancellations and lead time¶

Number of previous bookings not canceled and room price¶

Market segmentation and Average price per room¶

Booking status and Market segmentation¶

Special requirements and Booking status¶

Special requirement and Average price per room¶

Average price per room and Booking status¶

Lead time and Booking status¶

Arrival month and Booking status¶

Repeated guest and Booking status¶

Arrival month and Average price¶

➡ Multivariate Analysis¶

Avg Price Per Room vs Market Segment Type vs Booking Status¶

Average Price Per Room vs Arrival Month vs Booking Status¶

Average Price Per Room vs Lead Time vs Booking Status¶

No. of Adults vs Avg Price Per Room vs Booking Status¶

Avg Price Per Room vs Room Type Reserved vs Booking Status¶

No. of Special Requests vs Avg Price Per Room vs Booking Status¶

Arrival Year vs Arrival Month vs Avg Price Per Room¶

Lead Time vs No. of Weekend Nights vs Avg Price Per Room¶

Lead Time vs Market Segment Type vs Booking Status¶

No. of Children vs No. of Special Requests vs Booking Status¶

No. of Weekend Nights vs No. of Week Nights vs Avg Price Per Room¶

No. of Previous Cancellations vs Lead Time vs Booking Status¶

Market Segment Type vs Room Type Reserved vs Avg Price Per Room¶

Arrival Month vs No. of Special Requests vs Booking Status¶

Arrival Month vs Lead Time vs Booking Status¶

Market Segment Type vs Arrival Month vs Booking Status¶

No. of Adults vs Lead Time vs Avg Price Per Room¶

➡ EDA Questions¶

Question no :01¶

Question no :02¶

Question no :03¶

Question no :04¶

Question no :05¶

Question no :06¶

Key Observations of EDA¶

Observations¶

Recommendations¶

Data Processing¶

Model performance evaluation¶

Final Model Summary¶

Actionable Insights & Recommendations¶